149 citations · 278 across the 55 of their papers we have counts for
18 papers · 1 filter
CoSPlan: Corrective Sequential Planning via Scene Graph Incremental Updates
Shresth Grover, Priyank Pathak, Akash Kumar +1
Vision Language Models (VLMs) have shown promising planning capabilities, yet their success remains confined to the text domain, leaving visual decision-making relatively underexpl…
RobustGait: Robustness Analysis for Appearance Based Gait Recognition
Reeshoon Sayera, Akash Kumar, Sirshapan Mitra +2
Appearance-based gait recognition have achieved strong performance on controlled datasets, yet systematic evaluation of its robustness to real-world corruptions and silhouette vari…
OmViD: Omni-supervised active learning for video action detection
Aayush Rana, Akash Kumar, Vibhav Vineet +1
Video action detection requires dense spatio-temporal annotations, which are both challenging and expensive to obtain. However, real-world videos often vary in difficulty and may n…
GaitCrafter: Diffusion Model for Biometric Preserving Gait Synthesis
Sirshapan Mitra, Yogesh S. Rawat
Gait recognition is a valuable biometric task that enables the identification of individuals from a distance based on their walking patterns. However, it remains limited by the lac…
Re:Verse -- Can Your VLM Read a Manga?
Aaditya Baranwal, Madhav Kataria, Naitik Agrawal +2
Current Vision Language Models (VLMs) demonstrate a critical gap between surface-level recognition and deep narrative reasoning when processing sequential visual storytelling. Thro…
iSafetyBench: A video-language benchmark for safety in industrial environment
Raiyaan Abdullah, Yogesh Singh Rawat, Shruti Vyas
Recent advances in vision-language models (VLMs) have enabled impressive generalization across diverse video understanding tasks under zero-shot settings. However, their capabiliti…
Punching Bag vs. Punching Person: Motion Transferability in Videos
Raiyaan Abdullah, Jared Claypoole, Michael Cogswell +2
Action recognition models demonstrate strong generalization, but can they effectively transfer high-level motion concepts across diverse contexts, even within similar distributions…
DisenQ: Disentangling Q-Former for Activity-Biometrics
Shehreen Azad, Yogesh S Rawat
In this work, we address activity-biometrics, which involves identifying individuals across diverse set of activities. Unlike traditional person identification, this setting introd…
Colors See Colors Ignore: Clothes Changing ReID with Color Disentanglement
Priyank Pathak, Yogesh S. Rawat
Clothes-Changing Re-Identification (CC-ReID) aims to recognize individuals across different locations and times, irrespective of clothing. Existing methods often rely on additional…
MolVision: Molecular Property Prediction with Vision Language Models
Deepan Adak, Yogesh Singh Rawat, Shruti Vyas
Molecular property prediction is a fundamental task in computational chemistry with critical applications in drug discovery and materials science. While recent works have explored…
Coarse Attribute Prediction with Task Agnostic Distillation for Real World Clothes Changing ReID
Priyank Pathak, Yogesh S Rawat
This work focuses on Clothes Changing Re-IDentification (CC-ReID) for the real world. Existing works perform well with high-quality (HQ) images, but struggle with low-quality (LQ)…
HierarQ: Task-Aware Hierarchical Q-Former for Enhanced Video Understanding
Shehreen Azad, Vibhav Vineet, Yogesh Singh Rawat
Despite advancements in multimodal large language models (MLLMs), current approaches struggle in medium-to-long video understanding due to frame and context length limitations. As…
A Large-Scale Analysis on Contextual Self-Supervised Video Representation Learning
Akash Kumar, Ashlesha Kumar, Vibhav Vineet +1
Self-supervised learning has emerged as a powerful paradigm for label-free model pretraining, particularly in the video domain, where manual annotation is costly and time-intensive…
Scaling Open-Vocabulary Action Detection
Zhen Hao Sia, Yogesh Singh Rawat
In this work, we focus on scaling open-vocabulary action detection. Existing approaches for action detection are predominantly limited to closed-set scenarios and rely on complex,…
DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-ID
Xin Liang, Yogesh S Rawat
Clothes-changing person re-identification (CC-ReID) aims to recognize individuals under different clothing scenarios. Current CC-ReID approaches either concentrate on modeling body…
STPro: Spatial and Temporal Progressive Learning for Weakly Supervised Spatio-Temporal Grounding
Aaryan Garg, Akash Kumar, Yogesh S Rawat
In this work we study Weakly Supervised Spatio-Temporal Video Grounding (WSTVG), a challenging task of localizing subjects spatio-temporally in videos using only textual queries an…
LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models
Priyank Pathak, Shyam Marjit, Shruti Vyas +1
Visual-language foundation Models (FMs) exhibit remarkable zero-shot generalization across diverse tasks, largely attributed to extensive pre-training on largescale datasets. Howev…
Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding
Akash Kumar, Zsolt Kira, Yogesh Singh Rawat
In this work, we focus on Weakly Supervised Spatio-Temporal Video Grounding (WSTVG). It is a multimodal task aimed at localizing specific subjects spatio-temporally based on textua…